Wait, this is essentially a dataset! Here, we have three cases, one target variable (flavor), and 10 feature variables.

This is exactly the machine learning philosophy we mentioned in the first part. When ordering food, making decisions without tasting the flavor (the target variable, the expensive information) is risky. However, we can make predictions based on our experience (the model). Of course, not all ingredients (feature variables) are equally useful.

For example, olive oil and garlic are clearly important pieces of information for identifying the classic flavor.

For example…

For example… In other words, as long as we have good feature variables, training a good classifier becomes a piece of cake.

Obviously, chickpeas and lemon provide no useful information. This aligns with the principle we discussed in the previous lecture. These two variables have no variation in the dataset, so they don’t contribute any information.